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Application of machine learning methods for software effort prediction

Published: 11 May 2010 Publication History

Abstract

Software effort estimation is an important area in the field of software engineering. If the software development effort is over estimated it may lead to tight time schedules and thus quality and testing of software may be compromised. In contrast, if the software development effort is underestimated it may lead to over allocation of man power and resource. There are many models proposed in the literature for estimating software effort. In this paper, we analyze machine learning methods in order to develop models to predict software development effort we used Maxwell data consisting 63 projects. The results show that linear regression, MSP and M5Rules are effective methods for predicting software development effort.

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Cited By

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  • (2020)Applying Soft Computing Techniques for Software Project Effort Estimation ModellingNanoelectronics, Circuits and Communication Systems10.1007/978-981-15-7486-3_21(211-227)Online publication date: 18-Nov-2020
  • (2019)Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimationJournal of Software: Evolution and Process10.1002/smr.211731:2Online publication date: 14-Feb-2019
  • (2018)Prediction of Change-Prone Classes Using Machine Learning and Statistical TechniquesComputer Systems and Software Engineering10.4018/978-1-5225-3923-0.ch086(2043-2052)Online publication date: 2018
  • Show More Cited By

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Published In

cover image ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes  Volume 35, Issue 3
May 2010
151 pages
ISSN:0163-5948
DOI:10.1145/1764810
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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 May 2010
Published in SIGSOFT Volume 35, Issue 3

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Cited By

View all
  • (2020)Applying Soft Computing Techniques for Software Project Effort Estimation ModellingNanoelectronics, Circuits and Communication Systems10.1007/978-981-15-7486-3_21(211-227)Online publication date: 18-Nov-2020
  • (2019)Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimationJournal of Software: Evolution and Process10.1002/smr.211731:2Online publication date: 14-Feb-2019
  • (2018)Prediction of Change-Prone Classes Using Machine Learning and Statistical TechniquesComputer Systems and Software Engineering10.4018/978-1-5225-3923-0.ch086(2043-2052)Online publication date: 2018
  • (2018)Re-estimating software effort using prior phase efforts and data mining techniquesInnovations in Systems and Software Engineering10.1007/s11334-018-0311-z14:3(209-228)Online publication date: 1-Sep-2018
  • (2014)Prediction of Change-Prone Classes Using Machine Learning and Statistical TechniquesAdvanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability10.4018/978-1-4666-4490-8.ch019(193-202)Online publication date: 2014

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